SoroushVahidi/scidocs huggingface dataset:
Automated Generation of Event-Oriented Exploits in Android Hybrid Apps
(2018)

Recently more and more Android apps integrate the embedded browser, known as “WebView”, to render web pages and run JavaScript code without leaving these apps. WebView provides a powerful feature that allows event handlers defined in the native context (i.e., Java in Android) to handle web events that occur in WebView. However, as shown in prior work, this feature suffers from remote attacks, which we generalize as EventOriented Exploit (EOE) in this paper, such that adversaries may remotely access local critical functionalities through event handlers in WebView without any permission or authentication. In this paper, we propose a novel approach, EOEDroid, which can automatically vet event handlers in a given hybrid app using selective symbolic execution and static analysis. If a vulnerability is found, EOEDroid also automatically generates exploit code to help developers and analysts verify the vulnerability. To support exploit code generation, we also systematically study web events, event handlers and their trigger constraints. We evaluated our approach on 3,652 most popular apps. The result showed that our approach found 97 total vulnerabilities in 58 apps, including 2 cross-frame DOM manipulation, 53 phishing, 30 sensitive information leakage, 1 local resources access, and 11 Intent abuse vulnerabilities. We also found a potential backdoor in a high profile app that could be used to steal users’ sensitive information, such as IMEI. Even though developers attempted to close it, EOEDroid found that adversaries were still able to exploit it by triggering two events together and feeding event handlers with well designed input.
Encountering stronger password requirements: user attitudes and behaviors
(2010)

Text-based passwords are still the most commonly used authentication mechanism in information systems. We took advantage of a unique opportunity presented by a significant change in the Carnegie Mellon University (CMU) computing services password policy that required users to change their passwords. Through our survey of 470 CMU computer users, we collected data about behaviors and practices related to the use and creation of passwords. We also captured users' opinions about the new, stronger policy requirements. Our analysis shows that, although most of the users were annoyed by the need to create a complex password, they believe that they are now more secure. Furthermore, we perform an entropy analysis and discuss how our findings relate to NIST recommendations for creating a password policy. We also examine how users answer specific questions related to their passwords. Our results can be helpful in designing better password policies that consider not only technical aspects of specific policy rules, but also users' behavior in response to those rules.
A multiview 3D modeling system based on stereo vision techniques
(2004)

This paper introduces a stereo vision system to automatically generate 3D models of real objects. 3D model generation is based on the merging of multiview range images obtained from a digital stereo camera. Stereo images obtained from the camera are rectified, and a correlation-based stereo matching technique reconstructs range images from them. A turntable stage is also employed to obtain multiple range images of the objects. To register range images into a common coordinate system automatically, we introduce and calibrate a turntable coordinate system with respect to the camera coordinate system. After the registration of multiview range images, a 3D model is reconstructed using a volumetric integration technique. Error analysis on turntable calibration and 3D model reconstruction shows the accuracy of our 3D modeling system.
On the compensation of dead time and zero-current crossing for a PWM-inverter-controlled AC servo drive
(2004)

Conventional dead-time compensation methods, for pulsewidth-modulated (PWM) inverters, improve the current output waveforms; however, the zero-current crossing effect is still apparent. This letter proposes a new method, based on angle domain repetitive control, to reduce the distortions in the PWM inverters output waveforms caused by the dead time and the zero-crossing problem.
Design 13.56MHz 10 kW resonant inverter using GaN HEMT for wireless power transfer systems
(2017)

This paper presents the design process of a 13.56 MHz 10 kW and high efficiency inverter for wireless power transfer systems. The new 650V E-mode GaN is used in multiphase resonant topology to obtain high output power. The optimized efficiency design and the driver design are presented in detail. The circuit simulation results show that the inverter achieves 10 kW output power with the efficiency of over 98%. The experiment system is tested at 4 kW output power with the efficiency of 96.5%.
Correspondence-free pose estimation for 3D objects from noisy depth data
(2016)

Estimating the pose of objects from depth data is a problem of considerable practical importance for many vision applications. This paper presents an approach for accurate and efficient 3D pose estimation from noisy 2.5D depth images obtained from a consumer depth sensor. Initialized with a coarsely accurate pose, the proposed approach applies a hypothesize-and-test scheme that combines stochastic optimization and graphics-based rendering to refine the supplied initial pose, so that it accurately accounts for a sensed depth image. Pose refinement employs particle swarm optimization to minimize an objective function that quantifies the misalignment between the acquired depth image and a rendered one that is synthesized from a hypothesized pose with the aid of an object mesh model. No explicit correspondences between the depth data and the model need to be established, whereas pose hypothesis rendering and objective function evaluation are efficiently performed on the GPU. Extensive experimental results demonstrate the superior performance of the proposed approach compared to the ICP algorithm, which is typically used for pose refinement in depth images. Furthermore, the experiments indicate the graceful degradation of its performance to limited computational resources and its robustness to noisy and reduced polygon count models, attesting its suitability for use with automatically scanned object models and common graphics hardware.
Techniques for the Automated Analysis of Musical Audio
(2004)

This thesis presents work on automated analysis techniques for musical audio. A complete analysis of the content within an audio waveform is termed ‘transcription’ and can be used for intelligent, informed processing of the signal in a variety of applications. However, full transcription is beyond the ability of current methods and hence this thesis concerns subtasks of whole problem. A major part of musical analysis is the extraction of signal information from a time-frequency representation, often the short time Fourier transform or spectrogram. The ‘reassignment’ method is a technique for improving the clarity of these representations. It is comprehensively reviewed and its relationship to a number of classical instantaneous frequency measures is explicitly stated. Performance of reassignment as a sinusoidal frequency estimator is then assessed quantitatively for the first time. This is achieved in the framework of the Cramér-Rao bound. Reassignment is shown to be inferior to some other estimators for simple synthetic signals but achieves comparable results for real musical examples. Several extensions and uses of the reassignment method are then described. These include the derivation of reassignment measures extracted from the amplitude of the spectrogram, rather than the traditional phase-based method and the use of reassignment measures for signal classification. The second major area of musical analysis investigated in this thesis is beat tracking, where the aim is to extract both the tempo implied in the music and also its phase which is analagous to beat location. Three models for the beat in general music examples are described. These are cast in a state-space formulation within the Bayesian paradigm, which allows the use of Monte Carlo methods for inference of the model parameters. The first two models use pre-extracted note onsets and model tempo as either a linear Gaussian process or as Brownian motion. The third model also includes on-line detection of onsets, thus adding an extra layer of complexity. Sequential Monte Carlo algorithms, termed particle filters, are then used for the estimation of the data. The systems are tested on an extensive database, nearly three and a half hours in length and consisting of various styles of music. The results exceed the current published state of the art. The research presented here could form the early stages of a full transcription system, a proposal for which is also expounded. This would use a flow of contextual information from simpler, more global structures to aid the inference of more complex and detailed processes. The global structures present in the music (such as style, structure, tempo, etc.) still have their own uses, making this scheme instantly applicable to real problems.
A user's guide to tabu search
(1993)

We describe the main features of tabu search, emphasizing a perspective for guiding a user to understand basic implementation principles for solving combinatorial or nonlinear problems. We also identify recent developments and extensions that have contributed to increasing the efficiency of the method. One of the useful aspects of tabu search is the ability to adapt a rudimentary prototype implementation to encompass additional model elements, such as new types of constraints and objective functions. Similarly, the method itself can be evolved to varying levels of sophistication. We provide several examples of discrete optimization problems to illustrate the strategic concerns of tabu search, and to show how they may be exploited in various contexts. Our presentation is motivated by the emergence of an extensive literature of computational results, which demonstrates that a well-tuned implementation makes it possible to obtain solutions of high quality for difficult problems, yielding outcomes in some settings that have not been matched by other known techniques.
A simple hybrid 3-level buck-boost DC-DC converter with efficient PWM regulation scheme
(2015)

This paper describes a 3-level buck-boost converter with an output voltage range up to double the input voltage. The proposed converter can be used for applications that require stepping up/down the input voltage depending on the operating conditions like solar power harvesting. The converter has a hybrid structure between inductor-based converters and capacitor-based converters by utilizing a one flying capacitor and an inductor in the charge transfer process. The converter features smaller inductor size and current ripples as compared to the traditional buck-boost inductor-based converter while offering a high efficiency as compared to SC converters making it suitable for efficient on-chip integration. A prototype circuit has been designed on a 65nm technology using a 5-nH parasitic bondwire inductor. Simulation results show that the proposed converter features up to 20% improvement in the efficiency over a conventional 1:2 SC converter for a wide range of output voltage values.
Machine Learning and Inference Laboratory An Optimized Design of Finned-Tube Evaporators Using the Learnable Evolution Model
(2004)

Optimizing the refrigerant circuitry for a finned-tube evaporator is a daunting task for traditional exhaustive search techniques due to the extremely large number of circuitry possibilities. For this reason, more intelligent search techniques are needed. This paper presents and evaluates a novel optimization system, called ISHED1 (Intelligent System for Heat Exchanger Design). This system uses a recently developed non-Darwinian evolutionary computation method to seek evaporator circuit designs that maximize the capacity of the evaporator under given technical and environmental constraints. Circuitries were developed for an evaporator with three depth rows of 12 tubes each, based on optimizing the performance with uniform and non-uniform airflow profiles. ISHED1 demonstrated the capability to design an optimized circuitry for a non-uniform air distribution so the capacity showed no degradation over the traditional balanced circuitry design working with a uniform airflow.
Developing a retrieval based diagnostic aid for automated melanoma recognition of dermoscopic images
(2016)

Skin cancer is one of the most frequent cancers among human beings. Whereas, malignant melanoma is the most aggressive and deadly type of skin cancer, and its incidence has been quickly increasing over the last years. The detection of the malignant melanoma in its early stages with dermoscopic images reduces the mortality considerably, hence this a crucial issue for the dermatologists. However, their interpretation is time consuming and subjective, even for trained dermatologists. The current computer-aided diagnosis (CAD) systems are mainly noninteractive in nature and their prediction represents just a cue for the dermatologist, as the final decision regarding the likelihood of the presence of a melanoma is left exclusively to him/her. Recently, developing CAD schemes that use image retrieval approach to search for the clinically relevant and visually similar lesions has been attracting research interest. Although preliminary studies have suggested that using retrieval might improve dermatologists' performance and/or increase their confidence in the decision making, this technology is still in the early development stage with lack of benchmark evaluation in ground-truth datasets to compare retrieval effectiveness. A CAD system based on both classification and retrieval would be more effective and robust. This work is focusing on by addressing the various issues related to the development of such an integrated and interactive CAD system by performing automatic lesion segmentation with an adaptive thresholding and region growing approach, extracting invariant features from lesions and classifying and retrieving those using Extreme Learning Machines (ELM) and a similarity fusion approach. Finally, various methods are evaluated with promising results in a benchmark dataset of International Skin Imaging Collaboration (ISIC).
A Framework for Using Hypothesis-Driven Approaches to Support Data-Driven Learning Analytics in Measuring Computational Thinking in Block-Based Programming Environments
(2017)

Systematic endeavors to take computer science (CS) and computational thinking (CT) to scale in middle and high school classrooms are underway with curricula that emphasize the enactment of authentic CT skills, especially in the context of programming in block-based programming environments. There is, therefore, a growing need to measure students’ learning of CT in the context of programming and also support all learners through this process of learning computational problem solving. The goal of this research is to explore hypothesis-driven approaches that can be combined with data-driven ones to better interpret student actions and processes in log data captured from block-based programming environments with the goal of measuring and assessing students’ CT skills. Informed by past literature and based on our empirical work examining a dataset from the use of the Fairy Assessment in the Alice programming environment in middle schools, we present a framework that formalizes a process where a hypothesis-driven approach informed by Evidence-Centered Design effectively complements data-driven learning analytics in interpreting students’ programming process and assessing CT in block-based programming environments. We apply the framework to the design of Alice tasks for high school CS to be used for measuring CT during programming.
Perception on the Usage of Mobile Assisted Language Learning ( MALL ) in English as a Second Language ( ESL ) Learning among Vocational College Students
(2018)

Mobile phones are viewed as significant aids to English language learning. Numerous studies have been conducted in different contexts on mobile assisted language learning (MALL) that indicate the benefits of mobile devices for English language learning. Nevertheless, few studies on MALL had been conducted on among vocational college students in Malaysia. Therefore, this study was aimed to investigate the perception on the usage of MALL in English as a Second Language (ESL) among private vocational college students. Data were collected from a survey questionnaire adapted from Technology Acceptance Model (TAM). The results show the respondents have positive perception on the usage of MALL. Majority of the respondents showed overall agreement on both constructs perceived usefulness (PU) and perceived ease of use (PEoU) of MALL. They believed the usage of MALL will enhance the teaching and learning process. This evidence of acceptance has implication for educators and curriculum designers to exploit the mobile phone for autonomous and interactive ESL learning beyond the classroom context. It is hoped that MALL will be used as one of the teaching aids that could assist educators to teach English as a Second Language (ESL) more effectively.
A probabilistic fuzzy logic system for modeling and control
(2005)

In this paper, a probabilistic fuzzy logic system (PFLS) is proposed for the modeling and control problems. Similar to the ordinary fuzzy logic system (FLS), the PFLS consists of the fuzzification, inference engine and defuzzification operation to process the fuzzy information. Different to the FLS, it uses the probabilistic modeling method to improve the stochastic modeling capability. By using a three-dimensional membership function (MF), the PFLS is able to handle the effect of random noise and stochastic uncertainties existing in the process. A unique defuzzification method is proposed to simplify the complex operation. Finally, the proposed PFLS is applied to a function approximation problem and a robotic system. It shows a better performance than an ordinary FLS in stochastic circumstance.
CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms
(2011)

Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as “services” to endusers under a usage-based payment model. They can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behaviour modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited efforts. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter-networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter-networked Cloud computing scenarios. Several researchers from organisations such as HP Labs in USA are using CloudSim in their investigation on Cloud resource provisioning and energy-efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns.
CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets
(2000)

Association mining may often derive an undesirably large set of frequent itemsets and association rules. Recent studies have proposed an interesting alternative: mining frequent closed itemsets and their corresponding rules, which has the same power as association mining but substantially reduces the number of rules to be presented. In this paper, we propose an e cient algorithm, CLOSET, for mining closed itemsets, with the development of three techniques: (1) applying a compressed, frequent pattern tree FP-tree structure for mining closed itemsets without candidate generation, (2) developing a single pre x path compression technique to identify frequent closed itemsets quickly, and (3) exploring a partition-based projection mechanism for scalable mining in large databases. Our performance study shows that CLOSET is e cient and scalable over large databases, and is faster than the previously proposed methods.
A comparison of approaches to large-scale data analysis
(2009)

There is currently considerable enthusiasm around the MapReduce (MR) paradigm for large-scale data analysis [17]. Although the basic control flow of this framework has existed in parallel SQL database management systems (DBMS) for over 20 years, some have called MR a dramatically new computing model [8, 17]. In this paper, we describe and compare both paradigms. Furthermore, we evaluate both kinds of systems in terms of performance and development complexity. To this end, we define a benchmark consisting of a collection of tasks that we have run on an open source version of MR as well as on two parallel DBMSs. For each task, we measure each system's performance for various degrees of parallelism on a cluster of 100 nodes. Our results reveal some interesting trade-offs. Although the process to load data into and tune the execution of parallel DBMSs took much longer than the MR system, the observed performance of these DBMSs was strikingly better. We speculate about the causes of the dramatic performance difference and consider implementation concepts that future systems should take from both kinds of architectures.
Starfish: A Self-tuning System for Big Data Analytics
(2011)

Hadoop is a MAD system for data analytics  Magnetism: attracts all sources of data  Agility: adapts in sync with rapid data evolution  Depth: supports complex analytics needs Starfish makes Hadoop MADDER and Self-Tuning  Data-lifecycle-awareness: achieves good performance throughout data lifecycle  Elasticity: adjusts resources and operational costs  Robustness: provides availability and predictability Just-in-Time Job Optimization